Objective To evaluate the impact of cerebrovascular disease mortality on life expectancy （LE） in China in 2010 compared with 2005, and to identify the high-risk population （age, sex, and region） where cerebrovascu...Objective To evaluate the impact of cerebrovascular disease mortality on life expectancy （LE） in China in 2010 compared with 2005, and to identify the high-risk population （age, sex, and region） where cerebrovascular disease mortality has had a major impact on LE. Methods LE and cause-eliminated LE were calculated by using standard life tables which used adjusted mortality data from the Death Surveillance Data Sets in 2005 and 2010 from the National Disease Surveillance System. Decomposition was used to quantitate the impact of cerebrovascular disease in different age groups. Results LE in China was 73.24 years in 2010, which was higher in women and urban residents compared with men and rural residents. The loss of LE caused by cerebrovascular disease mortality was 2.26 years, which was higher in men and rural residents compared with women and urban residents. More than 30% of the loss of LE were attributed to premature death from cerebrovascular disease in people aged 〈65 years. Compared with 2005, LE in 2010 increased by 0.92 years. The reduction of cerebrovascular disease mortality in urban residents contributed 0.45 years to the increase of LE, but the increase of cerebrovascular disease mortality caused a 0.12-year loss of LE in rural residents. Conclusion Cerebrovascular disease mortality had a major impact on LE in China, with a significant difference between urban and rural residents. LE is likely to be further increased by reducing cerebrovascular disease mortality, and special attention should be paid to reducing premature deaths in people aged 〈65 years.展开更多
针对现有的码率自适应(adaptive bitrate,ABR)算法存在控制规则简单,不能有效提升用户体验质量(quality of experience,QoE),提出一种基于元学习的LABR(reinforcement learning based ABR)算法。采用策略梯度训练策略网络,利用元学习(me...针对现有的码率自适应(adaptive bitrate,ABR)算法存在控制规则简单,不能有效提升用户体验质量(quality of experience,QoE),提出一种基于元学习的LABR(reinforcement learning based ABR)算法。采用策略梯度训练策略网络,利用元学习(meta-learning)方法学习基线(baseline)函数来减少因网络吞吐量差异产生的方差,进一步提高模型的准确性和鲁棒性;通过在策略函数中加入熵损失方法提高累计期望奖励值。实验结果表明,LABR算法具有泛化性与鲁棒性,能有效提高用户的视频体验质量。展开更多
Long Range Wide Area Network (LoRaWAN) in the Internet ofThings (IoT) domain has been the subject of interest for researchers. Thereis an increasing demand to localize these IoT devices using LoRaWAN dueto the quickly...Long Range Wide Area Network (LoRaWAN) in the Internet ofThings (IoT) domain has been the subject of interest for researchers. Thereis an increasing demand to localize these IoT devices using LoRaWAN dueto the quickly growing number of IoT devices. LoRaWAN is well suited tosupport localization applications in IoTs due to its low power consumptionand long range. Multiple approaches have been proposed to solve the localizationproblem using LoRaWAN. The Expected Signal Power (ESP) basedtrilateration algorithm has the significant potential for localization becauseESP can identify the signal’s energy below the noise floor with no additionalhardware requirements and ease of implementation. This research articleoffers the technical evaluation of the trilateration technique, its efficiency,and its limitations for the localization using LoRa ESP in a large outdoorpopulated campus environment. Additionally, experimental evaluations areconducted to determine the effects of frequency hopping, outlier removal, andincreasing the number of gateways on localization accuracy. Results obtainedfrom the experiment show the importance of calculating the path loss exponentfor every frequency to circumvent the high localization error because ofthe frequency hopping, thus improving the localization performance withoutthe need of using only a single frequency.展开更多
基金supported by grant 2012CB517806 from the NationalProgram on Key Basic Research Project of Chin(973 Program)the US Centers for Disease Control and Prevention
文摘Objective To evaluate the impact of cerebrovascular disease mortality on life expectancy （LE） in China in 2010 compared with 2005, and to identify the high-risk population （age, sex, and region） where cerebrovascular disease mortality has had a major impact on LE. Methods LE and cause-eliminated LE were calculated by using standard life tables which used adjusted mortality data from the Death Surveillance Data Sets in 2005 and 2010 from the National Disease Surveillance System. Decomposition was used to quantitate the impact of cerebrovascular disease in different age groups. Results LE in China was 73.24 years in 2010, which was higher in women and urban residents compared with men and rural residents. The loss of LE caused by cerebrovascular disease mortality was 2.26 years, which was higher in men and rural residents compared with women and urban residents. More than 30% of the loss of LE were attributed to premature death from cerebrovascular disease in people aged 〈65 years. Compared with 2005, LE in 2010 increased by 0.92 years. The reduction of cerebrovascular disease mortality in urban residents contributed 0.45 years to the increase of LE, but the increase of cerebrovascular disease mortality caused a 0.12-year loss of LE in rural residents. Conclusion Cerebrovascular disease mortality had a major impact on LE in China, with a significant difference between urban and rural residents. LE is likely to be further increased by reducing cerebrovascular disease mortality, and special attention should be paid to reducing premature deaths in people aged 〈65 years.
基金supported by the MOE Project of Humanities and Social Sciences on the West and the Border Area (Grant No. 20XJC910001)the National Social Science Fund of China (Grant No. 21XTJ001)the National Natural Science Foundation of China (Grant No. 12001068)。
文摘针对现有的码率自适应(adaptive bitrate,ABR)算法存在控制规则简单,不能有效提升用户体验质量(quality of experience,QoE),提出一种基于元学习的LABR(reinforcement learning based ABR)算法。采用策略梯度训练策略网络,利用元学习(meta-learning)方法学习基线(baseline)函数来减少因网络吞吐量差异产生的方差,进一步提高模型的准确性和鲁棒性;通过在策略函数中加入熵损失方法提高累计期望奖励值。实验结果表明,LABR算法具有泛化性与鲁棒性,能有效提高用户的视频体验质量。
基金the ADEK Award for Research Excellence (AARE19-245)2019.
文摘Long Range Wide Area Network (LoRaWAN) in the Internet ofThings (IoT) domain has been the subject of interest for researchers. Thereis an increasing demand to localize these IoT devices using LoRaWAN dueto the quickly growing number of IoT devices. LoRaWAN is well suited tosupport localization applications in IoTs due to its low power consumptionand long range. Multiple approaches have been proposed to solve the localizationproblem using LoRaWAN. The Expected Signal Power (ESP) basedtrilateration algorithm has the significant potential for localization becauseESP can identify the signal’s energy below the noise floor with no additionalhardware requirements and ease of implementation. This research articleoffers the technical evaluation of the trilateration technique, its efficiency,and its limitations for the localization using LoRa ESP in a large outdoorpopulated campus environment. Additionally, experimental evaluations areconducted to determine the effects of frequency hopping, outlier removal, andincreasing the number of gateways on localization accuracy. Results obtainedfrom the experiment show the importance of calculating the path loss exponentfor every frequency to circumvent the high localization error because ofthe frequency hopping, thus improving the localization performance withoutthe need of using only a single frequency.
